https://github.com/demon-2-angel/neural-expert-system-for-credit-card-prediction
This project aims to predict credit scores using machine learning, enhancing financial literacy, promoting inclusion, and supporting financial planning. It analyzes credit history factors like payment history, utilization, and credit mix to provide reliable predictions.
https://github.com/demon-2-angel/neural-expert-system-for-credit-card-prediction
credit-card credit-worthiness expert-system machine-learning neural-expert-system
Last synced: 7 months ago
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This project aims to predict credit scores using machine learning, enhancing financial literacy, promoting inclusion, and supporting financial planning. It analyzes credit history factors like payment history, utilization, and credit mix to provide reliable predictions.
- Host: GitHub
- URL: https://github.com/demon-2-angel/neural-expert-system-for-credit-card-prediction
- Owner: Demon-2-Angel
- Created: 2024-07-20T13:26:46.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-20T16:16:54.000Z (about 1 year ago)
- Last Synced: 2025-03-13T05:32:11.403Z (7 months ago)
- Topics: credit-card, credit-worthiness, expert-system, machine-learning, neural-expert-system
- Language: Jupyter Notebook
- Homepage: https://neural-expert-system-for-credit-card-prediction.streamlit.app/
- Size: 2.41 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Neural Expert System for Credit Card Prediction
## Overview
This project aims to develop a neural expert system to predict credit scores using machine learning algorithms. The system analyzes various features related to credit card usage and financial behavior to determine the creditworthiness of individuals.
## Repository Structure
The repository contains the following files:
- `app.py`: The main application script that runs the web interface for the credit score prediction system.
- `multiapp.py`: A utility script that manages multiple applications within the main web interface.
- `about_us.py`: A script that provides information about the team and project.
- `credit_score_prediction.py`: The core script that contains the machine learning model and logic for credit score prediction.
## Understanding Credit History
Credit history is a record of a borrower's responsible repayment of debts. A credit report is a record of the borrower's credit history from various sources, including banks, credit card companies, collection agencies, and governments.
### Importance of Credit History
- **Loan Approval**: A good credit history increases your chances of loan approval.
- **Interest Rates**: Better credit history can help you secure lower interest rates.
- **Credit Limits**: Lenders may offer higher credit limits.
### Factors Affecting Credit History
- **Payment History**: Timely payment of bills.
- **Credit Utilization**: The ratio of your current debt to your credit limit.
- **Length of Credit History**: Longer credit history can positively impact your score.
- **New Credit**: Recent inquiries and newly opened accounts.
- **Credit Mix**: Types of credit accounts you have.
Understanding these factors can help you maintain a healthy credit history and improve your credit score.
## Installation
To run this project locally, follow these steps:
1. **Clone the repository**:
```bash
git clone https://github.com/Demon-2-Angel/Neural-Expert-System-for-Credit-Card-Prediction.git
cd Neural-Expert-System-for-Credit-Card-Prediction
```
2. **Create a virtual environment**:
```bash
python3 -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
This will start the web application on a local server. Open your web browser and navigate to the provided URL to interact with the credit score prediction system.
## Application Modules
### `app.py`
This script initializes and runs the Streamlit web application, integrating various modules to provide a user-friendly interface for credit score prediction.
### `multiapp.py`
This script provides functionality to manage multiple applications within the main Streamlit interface. It allows for the seamless integration of different components of the project.
### `about_us.py`
This script displays information about the project team and the objectives of the credit score prediction system. It serves as an informational page within the web application.
### `credit_score_prediction.py`
This script contains the machine learning model and prediction logic. It processes user input, applies the trained model, and returns a predicted credit score.
## About Us
### Our Mission
We aim to democratize access to financial insights and empower users with the tools they need to navigate the complexities of credit and lending. Our goals include:
1. **Enhancing Financial Literacy**
- Offering educational resources and tools to help users understand credit scoring and loan processes.
2. **Promoting Financial Inclusion**
- Providing fair and unbiased credit assessments to ensure everyone has access to credit opportunities.
3. **Supporting Financial Planning**
- Enabling users to make well-informed financial decisions with reliable data and predictive insights.
4. **Innovating Continuously**
- Staying at the forefront of technology by continuously improving our algorithms and models to deliver the best possible service.
### Why Choose Us?
1. **Cutting-Edge Technology**
- We use the latest AI and ML algorithms to deliver precise and reliable results.
2. **User-Centric Approach**
- Our tools are designed to be user-friendly and easy to understand.
3. **Transparency and Trust**
- We believe in transparent processes and aim to build trust through accuracy and integrity.
4. **Comprehensive Support**
- Our dedicated team is here to provide excellent support and guidance at every step.
### Meet the Team
- **Deekshit**
- **Aniruddha**
- **Sanika**
## Contributing
Contributions are welcome! Please fork the repository and create a pull request with your changes. For major changes, please open an issue first to discuss what you would like to change.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Contact
For any questions or suggestions, please contact [foraniruddhakumar@gmail.com].
Feel free to replace `[foraniruddhakumar@gmail.com]` with your actual contact email or other relevant contact information.
3. **Install dependencies**:
```bash
pip install -r requirements.txt
```
## Usage
To start the application, run the following command:
```bash
streamlit run app.py